An ethical multi-stakeholder recommender system based on evolutionary multi-objective optimization

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2 Citations (Scopus)

Abstract

In this work, we propose an ethical multi-stakeholder recommender system that uses a multi-objective evolutionary algorithm to make a trade-off between provider coverage, long-tail services inclusion, and recommendation accuracy. Experimental results on real-world datasets show that the proposed method significantly improves the novelty and diversity of recommended services and the coverage of providers with minor loss of accuracy.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 13th International Conference on Services Computing, SCC 2020
Place of PublicationLos Alamitos, Ca
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages478-480
Number of pages3
ISBN (Electronic)9781728187891
DOIs
Publication statusPublished - 2020
Event13th IEEE International Conference on Services Computing, SCC 2020 - Virtual, Beijing, China
Duration: 18 Oct 202024 Oct 2020

Publication series

NameProceedings of the IEEE International Conference on Services Computing SCC
PublisherIEEE COMPUTER SOC
ISSN (Print)2474-8137

Conference

Conference13th IEEE International Conference on Services Computing, SCC 2020
CountryChina
CityVirtual, Beijing
Period18/10/2024/10/20

Keywords

  • Diversity
  • Long-tail recommendation
  • Multi-objective evolutionary optimization
  • Multi-stakeholder recommender systems
  • P-fairness

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